Update app.py

app.py

@@ -1,27 +1,26 @@
 import gradio as gr
 import numpy as np
 import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+from diffusers import DiffusionPipeline, LoRA
 import torch
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
+model_repo_id = "stabilityai/sdxl-turbo"  # Replace with the base model you want to use
+lora_path = "lora.safetensors"  # Path to your LoRA model
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+# Set appropriate torch dtype
+torch_dtype = torch.float16 if device == "cuda" else torch.float32
 
+# Load base pipeline
 pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
 pipe = pipe.to(device)
 
+# Load and apply LoRA
+pipe.load_lora_weights(lora_path)
+
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
     prompt,
     negative_prompt,
@@ -36,7 +35,7 @@ def infer(
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
-    generator = torch.Generator().manual_seed(seed)
+    generator = torch.Generator(device).manual_seed(seed)
 
     image = pipe(
         prompt=prompt,
@@ -52,9 +51,9 @@ def infer(
 
 
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+    "Emoji of a smiling face with sunglasses, colorful background, 8k",
+    "An emoji riding a green horse",
+    "A delicious emoji-themed cheesecake",
 ]
 
 css = """
@@ -66,7 +65,7 @@ css = """
 
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
+        gr.Markdown(" # Text-to-Image Gradio Template with LoRA")
 
         with gr.Row():
             prompt = gr.Text(
@@ -105,7 +104,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=1024,
                 )
 
                 height = gr.Slider(
@@ -113,7 +112,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=1024,
                 )
 
             with gr.Row():
@@ -122,7 +121,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=0.0,
                     maximum=10.0,
                     step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
+                    value=7.5,
                 )
 
                 num_inference_steps = gr.Slider(
@@ -130,10 +129,11 @@ with gr.Blocks(css=css) as demo:
                     minimum=1,
                     maximum=50,
                     step=1,
-                    value=2,  # Replace with defaults that work for your model
+                    value=25,
                 )
 
         gr.Examples(examples=examples, inputs=[prompt])
+
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn=infer,